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Recently, object tracking has been widely studied as a binary classification problem. Semi-supervised learning is particularly suitable for improving classification accuracy when large quantities of unlabeled samples are generated (just like tracking procedure). The purpose of this paper is to fulfill robust and stable tracking by using collaborative learning, which belongs to the scope of semi-supervised...
The popularity of SVMs has grown tremendously in the last few years for many different classification problems due to its generalization properties, however training SVMs require high computational power. Platt's SMO is one the fastest algorithm for training support vector machines, which takes the decomposition technique to the extreme by selecting a set of only two points as the working set then...
This paper proposes a new face recognition approach by using Independent Component Analysis (ICA) and Ensemble Classifiers based on Support Vector Machine (SVM). Firstly, to improve the quality of the face images, a series of image pre-processing techniques are used. Then the ICA based on Kernel Principal Component Analysis (KPCA) and FastICA is employed to extract features. At last, appropriate classifiers...
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